Publications (7)
Probabilistic Conceptual Explainers: Trustworthy Conceptual Explanations for Vision Foundation Models
Hengyi Wang, Shiwei Tan, Hao Wang
Vision transformers (ViTs) have emerged as a significant area of focus, particularly for their capacity to be jointly trained with large language models and to serve as robust visi…
Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models
Hengyi Wang, Haizhou Shi, Shiwei Tan +6
Multimodal Large Language Models (MLLMs) have shown significant promise in various applications, leading to broad interest from researchers and practitioners alike. However, a comp…
Interpretable GOHR Agents via Sparse Autoencoders
Shiwei Tan, Yusong Zhao, Weiyi Qin +6
A central challenge in interpreting learned decision-making systems is to determine whether their internal representations contain concepts that help explain their behavior. We rep…
Variational Language Concepts for Interpreting Foundation Language Models
Hengyi Wang, Shiwei Tan, Zhiqing Hong +2
Foundation Language Models (FLMs) such as BERT and its variants have achieved remarkable success in natural language processing. To date, the interpretability of FLMs has primarily…
Overcoming the Curvature Bottleneck in MeanFlow
Xinxi Zhang, Shiwei Tan, Quang Nguyen +7
MeanFlow offers a promising framework for one-step generative modeling by directly learning a mean-velocity field, bypassing expensive numerical integration. However, we find that…
Causal Decoding for Hallucination-Resistant Multimodal Large Language Models
Shiwei Tan, Hengyi Wang, Weiyi Qin +3
Multimodal Large Language Models (MLLMs) deliver detailed responses on vision-language tasks, yet remain susceptible to object hallucination (introducing objects not present in the…